Operations | Monitoring | ITSM | DevOps | Cloud

Search powered AI and developer tools built for speed and scale Daily breakthroughs in large language models (LLMs) and generative AI have put developers at the forefront of the movement, influencing its direction and possibilities. In this blog, I’ll share how Elastic's search customers are using Elastic's vector database and open platform for search powered AI and developer tools to accelerate and scale generative AI experiences, giving them new avenues for growth.

AI has had a massive impact on every part of our lives, but mainly on how we consume large data sets easily. The observability world is based on collecting enormous amounts of data and consuming it by observing dashboards built on monitoring tools. Most of the o11y tasks like writing complex queries, creating dashboards & defining alerts, have been done much in the same way for the last decade & AI models are well-positioned to disrupt this modus operandi.

I had the pleasure to be invited by Canonical’s AI/ML Product Manager, Andreea Munteanu, to one of the recent episodes of the Canonical AI/ML podcast. As an enthusiast of automotive and technology with a background in software, I was very eager to share my insights into the influence of artificial intelligence (AI) in the automotive industry.

Ever since generative AI burst into public consciousness, business executives and IT leaders have been intrigued by the potential of generative AI to transform IT. As it turns out, generative AI has already begun revolutionizing ITOps and we are excited to be at the forefront of this game-changing technology through the ScienceLogic SL1 Hollywood release.

This blog post explores the technical and strategic benefits of deploying open-source AI models on Ubuntu. We’ll highlight why it makes sense to use Ubuntu with open-source AI models, and outline the deployment process on Azure.

From its initial appearance in the dev-tools space, GenAI has had an outsized impact on how developers approach day-to-day tasks (just ask any developer about when they first started using GitHub’s copilot). While any risks are still being evaluated—like potential for introducing anti-patterns or inadvertently running afoul of compliance requirements, many engineering teams have successfully implemented GenAI with measurable gains in collaboration and productivity.

In today’s digital landscape, delivering a flawless customer experience is the ultimate competitive advantage. However, traditional methods of ensuring service resilience during operation can often be both expensive and cumbersome to maintain. This is where left-shift operations come into play—a powerful strategy aimed at instilling quality and resiliency in the early stages of building and delivering high-quality products and services..